library(tidyverse)
library(rlang)
library(lubridate)
library(scales)
library(ggrepel)
library(glue)
library(rvest)
library(pander)
library(plotly)
library(QuantTools)
library(jsonlite)
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("missing", "")
theme_set(theme_bw())
auStates <- c(
  ACT = "Australian Capital Territory",
  QLD = "Queensland",
  NSW = "New South Wales",
  VIC = "Victoria",
  SA = "South Australia",
  WA = "Western Australia",
  NT = "Northern Territory",
  TAS = "Tasmania"
)
ausPops <- tribble(
  ~State, ~Population,
  "New South Wales",    8117976,
  "Victoria", 6629870,
  "Queensland", 5115451,
  "South Australia", 1756494,
  "Western Australia", 2630557,
  "Tasmania", 535500,
  "Northern Territory", 245562,
  "Australian Capital Territory", 428060
)

Data Sources

data <- fromJSON("https://covidlive.com.au/covid-live.json") %>%
    as_tibble() %>%
    mutate(
      across(
        .cols = ends_with("CNT"), 
        .fns = as.numeric
      ),
      REPORT_DATE = ymd(REPORT_DATE)
    )
dt <- data %>%
    dplyr::filter(
        CODE == "AUS", !is.na(LAST_UPDATED_DATE)
    ) %>%
    pull(REPORT_DATE) %>%
    max()
getCovidTable <- function(
  state = c("ACT", "QLD", "NSW", "VIC", "SA", "WA", "NT", "TAS"),
  type = c("cases", "recoveries", "deaths", "active-cases", "tests"),
  .state_full = auStates
){
  
  state <- match.arg(state)
  
  type <- match.arg(type)
  type_name <- c(
    cases = "confirmed",
    recoveries = "recovered",
    deaths = "deaths",
    `active-cases` = "active",
    tests = "tests"
  )[[type]]
  col_name <- c(
    cases = "CASES",
    recoveries = "RECOV",
    deaths = "DEATHS",
    `active-cases` = "ACTIVE",
    tests = "TESTS"
  )[[type]]

    url <- glue("https://covidlive.com.au/report/daily-{type}/{str_to_lower(state)}")
  url %>%
    read_html() %>%
    html_nodes("body") %>%
    xml_find_all(
      glue("//table[contains(@class, 'DAILY-{str_to_upper(type)}')]")
    ) %>% 
    html_table() %>%
    .[[1]] %>%
    as_tibble() %>%
    dplyr::select(date = DATE, !!sym(type_name) := !!sym(col_name)) %>%
    separate(date, into = c("day", "month")) %>%
    mutate(
      year = case_when(
        month %in% c("Jan") & day < 25 ~ 2021L,
        TRUE ~ 2020L
      ),
      date = paste(year, month, day, sep = "-"),
      date = parse_date_time(date, orders = "%Y-%B-%d"),
      date = ymd(date),
      State = .state_full[str_to_upper(state)],
      Country = "Australia"
    ) %>%
    mutate_at(
      .vars = type_name, 
      .funs = function(x){as.integer(str_remove_all(x, ","))}
    ) %>%
    dplyr::select(State, Country, date, !!sym(type_name)) %>%
    arrange(date) 
}
confirmed <- names(auStates) %>%
  lapply(getCovidTable, type = "cases") %>%
  bind_rows()
recovered <- names(auStates) %>%
  lapply(getCovidTable, type = "recoveries") %>%
  bind_rows()
deaths <- names(auStates) %>%
  lapply(getCovidTable, type = "deaths") %>%
  bind_rows()
active <- names(auStates) %>%
  lapply(getCovidTable, type = "active-cases") %>%
  bind_rows()
tested <- names(auStates) %>%
  lapply(getCovidTable, type = "tests") %>%
  bind_rows() %>%
  dplyr::filter(!is.na(tests))
getLocal <- function(  
  state = c("ACT", "QLD", "NSW", "VIC", "SA", "WA", "NT", "TAS"),  
  .state_full = auStates
){
  
  state <- match.arg(state)
  
  url <- glue("https://covidlive.com.au/report/daily-source-overseas/{str_to_lower(state)}")
  url %>%
    read_html() %>%
    html_nodes("body") %>%
    xml_find_all("//table[contains(@class, 'DAILY-SOURCE-OVERSEAS')]") %>% 
    html_table() %>%
    .[[1]] %>%
    as_tibble() %>%
    dplyr::select(date = DATE, local = LOCAL) %>%
    separate(date, into = c("day", "month")) %>%
    mutate(
      year = case_when(
        month %in% c("Jan") & day < 25 ~ 2021L,
        TRUE ~ 2020L
      ),
      date = paste(year, month, day, sep = "-"),
      date = parse_date_time(date, orders = "%Y-%B-%d"),
      date = ymd(date),
      State = .state_full[str_to_upper(state)],
      Country = "Australia",
      local = str_remove_all(local, ","),
      local = as.integer(local)
    ) %>%
    dplyr::select(State, Country, date, local) %>%
    arrange(date) 
}
local <- names(auStates) %>%
  lapply(getLocal) %>%
  bind_rows() %>%
  dplyr::filter(!is.na(local))
vic_aged <- "https://covidlive.com.au/report/daily-outbreaks-aged" %>%
  read_html() %>%
  html_nodes("body") %>%
  xml_find_all("//table[contains(@class, 'DAILY-OUTBREAKS-AGED')]") %>% 
    html_table() %>%
    .[[1]] %>%
    as_tibble() %>%
    dplyr::select(date = DATE, deaths = DEATHS, active = ACTIVE) %>%
    separate(date, into = c("day", "month")) %>%
    mutate(
      year = case_when(
        month %in% c("Jan") & day < 25 ~ 2021L,
        TRUE ~ 2020L
      ),
      date = paste(year, month, day, sep = "-"),
      date = parse_date_time(date, orders = "%Y-%b-%d"),
      date = ymd(date),
      State = "Victoria (Aged Care)",
      Country = "Australia"
    ) %>%
  arrange(date) %>%
  mutate(
    deaths = na_locf(deaths),
    active = na_locf(active),
    new = c(active[[1]], diff(active)),
    new = ifelse(new < 0, 0, new),
    confirmed = cumsum(new),
    recovered = confirmed - deaths - active,
    recovered = ifelse(recovered < 0, 0, recovered)
  ) %>%
  dplyr::filter(date <= dt) %>%
  dplyr::select(
    State, Country, date, deaths, confirmed, active, recovered
  ) 

Data for confirmed cases, active cases, recoveries and fatalities was exclusively sourced from COVID LIVE. Reliable data sources were extremely challenging prior to April and as such, minimal data is available from the early stages of the outbreak.

Similarly, data for the Victorian Aged Care outbreak was obtained from data based on press releases.

International Data

International data and figures can be viewed here

Latest Australian Data

Australian State populations were taken from the ABS Website and were accurate in Sept 2019.

  • Using an estimated population size of 25,459,470, the total percentage of the Australian population confirmed as having been infected currently sits at 0.11%, or one person in every 889.
  • Within Victoria, that rises to one in every 325 having contracted the virus at some point
data %>%
    dplyr::filter(
        REPORT_DATE == dt
    ) %>%
    dplyr::mutate(
        Increase = CASE_CNT - PREV_CASE_CNT,
        `% Increase` = percent(Increase / PREV_CASE_CNT, accuracy = 0.1),
        `Fatality Rate` = percent(DEATH_CNT / CASE_CNT, accuracy = 0.1),
        `Recovery Rate` = percent(RECOV_CNT / CASE_CNT, accuracy = 0.1),
        State = case_when(
            CODE == "AUS" ~ "National Total",
            TRUE ~ auStates[CODE]
        ),
        State = factor(State, levels = c(dplyr::arrange(ausPops, desc(Population))$State, "National Total"))
    ) %>%
    dplyr::rename(
        Fatalities = DEATH_CNT,
        Recovered = RECOV_CNT,
        `Currently Active` = ACTIVE_CNT
    ) %>%
    dplyr::select(
        State,
        PREV_CASE_CNT, CASE_CNT,
        contains("Increase"),
        contains("Fatal"),
        contains("Recov", ignore.case = FALSE),
        `Currently Active`
    ) %>%
    dplyr::arrange(State) %>%
    setNames(
        str_replace_all(names(.), "PREV_CASE_CNT", as.character(dt - 1))
    ) %>%
    setNames(
        str_replace_all(names(.), "CASE_CNT", as.character(dt))
    ) %>%
    pander(
        justify = "lrrrrrrrrr",
        caption = paste(
            "*Confirmed cases, fatalities and recoveries reported by each state at time of preparation.*"
        ),
        emphasize.strong.rows = nrow(.)
    )
Confirmed cases, fatalities and recoveries reported by each state at time of preparation.
State 2021-01-11 2021-01-12 Increase % Increase Fatalities Fatality Rate Recovered Recovery Rate Currently Active
New South Wales 5,018 5,034 16 0.3% 54 1.1% 3,216 63.9% 200
Victoria 20,411 20,411 0 0.0% 820 4.0% 19,553 95.8% 38
Queensland 1,278 1,281 3 0.2% 6 0.5% 1,243 97.0% 26
Western Australia 877 878 1 0.1% 9 1.0% 855 97.4% 14
South Australia 588 588 0 0.0% 4 0.7% 569 96.8% 15
Tasmania 234 234 0 0.0% 13 5.6% 221 94.4% 0
Australian Capital Territory 118 118 0 0.0% 3 2.5% 115 97.5% 0
Northern Territory 90 90 0 0.0% 0 0.0% 71 78.9% 19
National Total 28,614 28,634 20 0.1% 909 3.2% 25,843 90.3% 312

Plot of Current Australian Values

ausStatsCap <- "*Current confirmed and recovered cases, along with fatalities for Australia only. Active cases are shown as confirmed cases excluding fatalities and those classed as recovered. Some data regarding recovered cases prior to 1^st^ May 2020 may be estimates.*"
ggplotly(
  data %>% 
    dplyr::filter(CODE == "AUS", REPORT_DATE <= dt, REPORT_DATE > "2020-03-01") %>% 
    dplyr::select(
      Date = REPORT_DATE, 
      Confirmed = CASE_CNT,
      Active = ACTIVE_CNT, 
      Fatal = DEATH_CNT, 
      Recovered = RECOV_CNT
    ) %>% 
    mutate(
      Active = case_when(
        is.na(Active) ~ Confirmed - Fatal - Recovered,
        TRUE ~ Active
      )
    )  %>%
    pivot_longer(
      cols = c("Active", "Fatal", "Recovered"), 
      names_to = "Status", values_to = "Total"
    ) %>%
    mutate(
      Status = factor(Status, levels = c("Fatal", "Recovered", "Active"))
    ) %>%
    ggplot(aes(Date, Total, fill = Status)) +
    geom_col() +
    geom_line(
      data = . %>%
        group_by(Date) %>%
        summarise(
          Total = sum(Total)
        ) %>%
        mutate(Status = "Confirmed"),
      colour = "blue"
    ) +
    scale_fill_manual(
      values = c(
        Active = rgb(0, 0, 0),
        Confirmed = rgb(0, 0.3, 0.7),
        Fatal = rgb(0.8, 0.2, 0.2),
        Recovered = rgb(0.2, 0.7, 0.4)
      )
    ) +
    scale_x_date(expand = expansion(c(0, 0.03))) +
    scale_y_continuous(expand = expansion(c(0, 0.05))) +
    labs("Total Cases")
)

Current confirmed and recovered cases, along with fatalities for Australia only. Active cases are shown as confirmed cases excluding fatalities and those classed as recovered. Some data regarding recovered cases prior to 1st May 2020 may be estimates.

ggplotly(
  data %>% 
    dplyr::filter(CODE != "AUS", REPORT_DATE <= dt, REPORT_DATE > "2020-03-01") %>% 
    dplyr::select(
      CODE,
      Date = REPORT_DATE, 
      Confirmed = CASE_CNT,
      Active = ACTIVE_CNT, 
      Fatal = DEATH_CNT, 
      Recovered = RECOV_CNT
    ) %>% 
    mutate(
      Active = case_when(
        is.na(Active) ~ Confirmed - Fatal - Recovered,
        TRUE ~ Active
      ),
      State = auStates[CODE]
    )  %>%
    pivot_longer(
      cols = c("Active", "Fatal", "Recovered"), 
      names_to = "Status", values_to = "Total"
    ) %>%
    left_join(ausPops) %>%
    mutate(
      Status = factor(Status, levels = c("Fatal", "Recovered", "Active")),
      Rate = 1e6*Total / Population
    ) %>%
    ggplot(aes(Date, Rate, fill = Status, label = Total)) +
    geom_col() +
    geom_line(
      data = . %>%
        group_by(State, Date) %>%
        summarise(
          Rate = sum(Rate),
          Total = sum(Total)
        ) %>%
        mutate(Status = "Confirmed"),
      colour = "blue"
    ) +
    facet_wrap(~State, ncol = 4) + 
    scale_fill_manual(
      values = c(
        Active = rgb(0, 0, 0),
        Confirmed = rgb(0, 0.3, 0.7),
        Fatal = rgb(0.8, 0.2, 0.2),
        Recovered = rgb(0.2, 0.7, 0.4)
      )
    ) +
    scale_x_date(expand = expansion(c(0, 0.03))) +
    labs(y = "Rate (Cases / Million)")
)

Breakdown of individual states. Victorian recovered numbers began to be accurately reported from 22nd March, with other states gradually providing this information. NSW/QLD recovered cases have only recently begun being reported and up until the most recent dates, recovered/active values were very approximate for these states. The extreme drop for NSW active cases in early June is a function of the changed reporting strategy implemented by NSW Health.

Daily New Cases

ggplotly(
  confirmed %>%
    dplyr::filter(date <= dt) %>%    
    group_by(State) %>%
    mutate(daily = c(0, diff(confirmed))) %>%
    ungroup() %>%
    dplyr::filter(confirmed > 0) %>%
    mutate(
      daily = case_when(
        daily < 0 ~ 0,
        daily >= 0 ~ daily
      )
    ) %>%
    bind_rows(
      group_by(., date) %>%
        summarise(daily = sum(daily)) %>%
        ungroup() %>%
        mutate(State = "All States")
    ) %>%
    group_by(State) %>%
    mutate(
      MA = round(sma(daily, 7), 2),
      MA2 = round(sma(daily, 14), 2),
      `Above Average` = MA > MA2
    ) %>%
    dplyr::filter(date > "2020-03-01") %>%
    ggplot(aes(date, daily)) +
    geom_col(
      aes(fill = `Above Average`, colour = `Above Average`),
      data = . %>% dplyr::filter(!is.na(`Above Average`)),
      width = 1/2
    ) +
    geom_line(aes(y = MA), colour = "blue") +
    geom_line(aes(y = MA2), colour = "black") +
    facet_wrap(~State, scales = "free_y") +
    labs(
      x = "Date",
      y = "Daily New Cases",
      fill = "\nAbove\nAverage"
    ) +
    scale_fill_manual(values = c("white", rgb(1, 0.2, 0.2))) +
    scale_colour_manual(values = c("grey50", rgb(1, 0.2, 0.2))),
  tooltip = c(
    "date", "daily", "MA"
  )
)

Daily new cases for each state shown against the 7-day (blue) and 14-day (black) averages. Days which the 7-day average is above the 14-day average are highlighted in red.

Australian Fatality Rate

inc <- 6
icu <- 11
d <- 7
offset <- icu + d 
minDate <- "2020-04-20"
list(
  confirmed %>%
    dplyr::filter(date <= dt) %>%    
    group_by(date) %>%
    summarise_at("confirmed", sum) %>%
    left_join(
      deaths %>%
        group_by(date) %>%
        summarise_at("deaths", sum)
    ) %>%
    dplyr::filter(
      date > minDate
    ) %>%
    mutate(
      fr = deaths / confirmed,
      type = "No Offset"
    ),
  confirmed %>%
    dplyr::filter(date <= dt) %>%    
    mutate(
      date = date + offset 
    ) %>%
    group_by(date) %>%
    summarise_at("confirmed", sum) %>%
    left_join(
      deaths %>%
        group_by(date) %>%
        summarise_at("deaths", sum) 
    ) %>%
    dplyr::filter(
      date > minDate
    ) %>%
    mutate(
      fr = deaths / confirmed,
      type = glue("Offset ({offset} days)")
    ) 
) %>%
  bind_rows() %>%
  ggplot(
    aes(date, fr, colour = type)
  ) +
  geom_line() +
  scale_x_date(
    expand = expansion(mult = 0, add = 0)
  ) +
  scale_y_continuous(label = percent) +
  labs(
    x = "Date",
    y = "Estimated Fatality Rate",
    colour = "Calculation"
  )
*Fatality rate for Australian cases as calculated using two methods.
Where no offset is included, the rate shown is simply the number of fatalities divided by the total number of reported cases on the same date.
When cases increase during a new outbreak, this will skew the fatality rate lower.
An alternative is to use an offset based on the fact the the median time from infection to symptom onset is 6 days, the median time from symptom onset to ICU admission is 11 days, and the median time from ICU admission to mortality is 7 days.
When using the offset, the fatality rate is calculated as the number of recorded fatalities on a given date, divided by by the number of cases from 18 days ago.
Whilst still flawed this may give a less biased estimate on the true fatality rate, and importantly, will always be higher than the alternative calculation.
The intial fatality rate spiked above 30% during the intial outbreak under the offset approach, and as such, data is only shown after 20 Apr, 2020.
All times used for estimation the offset were obtained from [here](https://wwwnc.cdc.gov/eid/article/26/6/20-0320_article)*

Fatality rate for Australian cases as calculated using two methods. Where no offset is included, the rate shown is simply the number of fatalities divided by the total number of reported cases on the same date. When cases increase during a new outbreak, this will skew the fatality rate lower. An alternative is to use an offset based on the fact the the median time from infection to symptom onset is 6 days, the median time from symptom onset to ICU admission is 11 days, and the median time from ICU admission to mortality is 7 days. When using the offset, the fatality rate is calculated as the number of recorded fatalities on a given date, divided by by the number of cases from 18 days ago. Whilst still flawed this may give a less biased estimate on the true fatality rate, and importantly, will always be higher than the alternative calculation. The intial fatality rate spiked above 30% during the intial outbreak under the offset approach, and as such, data is only shown after 20 Apr, 2020. All times used for estimation the offset were obtained from here

Current Growth Factor

n <- 14
minCases <- 1
cp <- glue(
  "*Growth factor for each State/Territory. 
  __Values are calculated using only locally-acquired cases__.
  In order to try and minimise volatility a {n} day simple moving average was used, in contrast to the 5 day average as advocated [here](https://www.abc.net.au/news/2020-04-10/coronavirus-data-australia-growth-factor-covid-19/12132478).
  This enables assessment of the growth factor over an entire quarantine period.
  This value becomes volatile when daily new cases approach zero as is commonly observed in small populations, and at the end stages of an outbreak. 
  As a result, values are only shown when the {n}-day average of new __locally acquired cases__ exceeds {minCases}.*"
)
gf <- list(
  local %>%
    dplyr::filter(date <= dt) %>%    
    arrange(date) %>%
    group_by(State) %>%
    mutate(
      new = c(0, diff(local)),
      new_ma = sma(new, n)
    ) %>%
    dplyr::filter(local > 0, !is.na(new_ma)) %>%
    mutate(
      R = c(NA, new_ma[-1] / new_ma[-n()]),
      R = case_when(
        is.nan(R) ~ NA_real_,
        new_ma < minCases ~ NA_real_,
        TRUE ~ R
      )
    ) %>%
    ungroup() %>%
    arrange(State),
  local %>%
    dplyr::filter(date <= dt) %>%
    arrange(date) %>%
    group_by(Country, date) %>%
    summarise_at(vars(local), sum) %>%
    ungroup() %>%
    mutate(
      new = c(0, diff(local)),
      new_ma = sma(new, n)
    ) %>%
    dplyr::filter(local > 0, !is.na(new_ma)) %>%
    mutate(
      R = c(NA, new_ma[-1] / new_ma[-n()]),
      R = case_when(
        is.nan(R) ~ NA_real_,
        new_ma < minCases ~ NA_real_,
        TRUE ~ R
      ),
      State = "All States"
    ) %>%
    arrange(State)
) %>%
  bind_rows() %>%
  # dplyr::filter(date > ymd("2020-03-01")) %>%
  ggplot(aes(date, R, colour = State)) +
  geom_ribbon(aes(ymin = 1, ymax = R), alpha = 0.1) +
  geom_hline(yintercept = 1) +
  geom_label(
    aes(label = R),
    data = . %>%
      dplyr::filter(date == max(date), !is.na(R)) %>%
      mutate(R = round(R, 2), date = date + 1),
    fill = rgb(1, 1, 1, 0.3),
    show.legend = FALSE,
    nudge_y = 0.3,
    size = 4
  ) +
  labs(
    x = "Date", y = "Growth Factor"
  ) +
  facet_wrap(~State, scales = "free_x") +
  theme(legend.position = "none") +
  coord_cartesian(ylim = c(0.5, 1.8))#2.1))
gf
*Growth factor for each State/Territory. 
__Values are calculated using only locally-acquired cases__.
In order to try and minimise volatility a 14 day simple moving average was used, in contrast to the 5 day average as advocated [here](https://www.abc.net.au/news/2020-04-10/coronavirus-data-australia-growth-factor-covid-19/12132478).
This enables assessment of the growth factor over an entire quarantine period.
This value becomes volatile when daily new cases approach zero as is commonly observed in small populations, and at the end stages of an outbreak. 
As a result, values are only shown when the 14-day average of new __locally acquired cases__ exceeds 1.*

Growth factor for each State/Territory. Values are calculated using only locally-acquired cases. In order to try and minimise volatility a 14 day simple moving average was used, in contrast to the 5 day average as advocated here. This enables assessment of the growth factor over an entire quarantine period. This value becomes volatile when daily new cases approach zero as is commonly observed in small populations, and at the end stages of an outbreak. As a result, values are only shown when the 14-day average of new locally acquired cases exceeds 1.

The current 14 day growth factor is 1.02 which gives considerable cause for concern..

Testing Within Each State

tested %>% 
  left_join(confirmed, by = c("State", "Country", "date") ) %>%
  dplyr::filter(date == dt) %>%
  left_join(ausPops,  by = "State") %>%
  bind_rows(
    tibble(
      State = "National Total",
      date = dt,
      Population = sum(.$Population, na.rm = TRUE),
      confirmed = sum(.$confirmed, na.rm = TRUE),
      tests = sum(.$tests, na.rm = TRUE)
    )
  ) %>%
  mutate(
    `Tests / '000` = round(1e3 * tests / Population, 2),
    Positive = confirmed / tests,
    Negative = 1 - Positive,
    isTotal = grepl("Total", State)
  ) %>%
  dplyr::select(
    State, Population,
    Confirmed = confirmed,
    Tests = tests, 
    contains("000"), 
    ends_with("ive"),
    isTotal
  ) %>%
  arrange(isTotal, desc(`Tests / '000`)) %>%
  dplyr::select(-isTotal) %>%
  dplyr::rename(
    `% Positive Tests` = Positive,
    `% Negative Tests` = Negative
  ) %>%
  mutate_at(
    vars(starts_with("%")), percent, accuracy = 0.01
  ) %>%
  pander(
    justify = "lrrrrrr",
    missing = "",
    caption = glue(
      "*COVID-19 testing scaled by state population size.
      Confirmed cases are assumed to be the tests returning a positive result.
      The current numbers available for some states are a lower limit, and as such, the proportion of the population tested is likely to be higher, as is the proportion of tests returning a negative result.*"
    ),
    emphasize.strong.rows = nrow(.)
  )
COVID-19 testing scaled by state population size. Confirmed cases are assumed to be the tests returning a positive result. The current numbers available for some states are a lower limit, and as such, the proportion of the population tested is likely to be higher, as is the proportion of tests returning a negative result.
State Population Confirmed Tests Tests / '000 % Positive Tests % Negative Tests
Victoria 6,629,870 20,411 4,176,707 630 0.49% 99.51%
New South Wales 8,117,976 5,034 4,388,407 540.6 0.11% 99.89%
South Australia 1,756,494 588 893,844 508.9 0.07% 99.93%
Northern Territory 245,562 90 88,324 359.7 0.10% 99.90%
Australian Capital Territory 428,060 118 148,660 347.3 0.08% 99.92%
Queensland 5,115,451 1,281 1,621,595 317 0.08% 99.92%
Tasmania 535,500 234 150,329 280.7 0.16% 99.84%
Western Australia 2,630,557 878 670,126 254.8 0.13% 99.87%
National Total 25,459,470 28,634 12,137,992 476.8 0.24% 99.76%

R Session Information

R version 4.0.3 (2020-10-10)

**Platform:** x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=C, LC_NUMERIC=C, LC_TIME=C, LC_COLLATE=C, LC_MONETARY=C, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: jsonlite(v.1.7.2), QuantTools(v.0.5.7.1), data.table(v.1.13.6), plotly(v.4.9.3), pander(v.0.6.3), rvest(v.0.3.6), xml2(v.1.3.2), glue(v.1.4.2), ggrepel(v.0.9.0), scales(v.1.1.1), lubridate(v.1.7.9.2), rlang(v.0.4.10), forcats(v.0.5.0), stringr(v.1.4.0), dplyr(v.1.0.2), purrr(v.0.3.4), readr(v.1.4.0), tidyr(v.1.1.2), tibble(v.3.0.4), ggplot2(v.3.3.3) and tidyverse(v.1.3.0)

loaded via a namespace (and not attached): Rcpp(v.1.0.5), ps(v.1.5.0), assertthat(v.0.2.1), digest(v.0.6.27), R6(v.2.5.0), cellranger(v.1.1.0), backports(v.1.2.1), reprex(v.0.3.0), evaluate(v.0.14), highr(v.0.8), httr(v.1.4.2), pillar(v.1.4.7), lazyeval(v.0.2.2), curl(v.4.3), readxl(v.1.3.1), rstudioapi(v.0.13), rmarkdown(v.2.6), labeling(v.0.4.2), selectr(v.0.4-2), htmlwidgets(v.1.5.3), munsell(v.0.5.0), broom(v.0.7.3), compiler(v.4.0.3), modelr(v.0.1.8), xfun(v.0.20), pkgconfig(v.2.0.3), htmltools(v.0.5.0), tidyselect(v.1.1.0), fasttime(v.1.0-2), fansi(v.0.4.1), viridisLite(v.0.3.0), crayon(v.1.3.4), dbplyr(v.2.0.0), withr(v.2.3.0), grid(v.4.0.3), gtable(v.0.3.0), lifecycle(v.0.2.0), DBI(v.1.1.0), magrittr(v.2.0.1), cli(v.2.2.0), stringi(v.1.5.3), farver(v.2.0.3), fs(v.1.5.0), ellipsis(v.0.3.1), generics(v.0.1.0), vctrs(v.0.3.6), tools(v.4.0.3), crosstalk(v.1.1.0.1), hms(v.0.5.3), yaml(v.2.2.1), colorspace(v.2.0-0), knitr(v.1.30) and haven(v.2.3.1)